I was always interested in both Physics and Economics and did studies in both disciplines.
As a physicist, the models that I encountered in economic theory always left me quite disappointed. The main reason for this was that -like the article says- most of the models used in Economics are based on abstract reasoning about "ideal" markets and actors (or more recently on game-theoretic ideas) instead of experimental data. One reason for this is of course that when many of those models were developed (in the 50s or earlier) there simply was no reliable (micro-)data available that one could develop a theory against. Another big problem that kept Economics from taking a more experimental stance towards model generation is of course that until recently it was very hard or outright impossible to conduct large-scale experiments, which are the main instrument to validate (or better, not falsify) a given theory in other disciplines such as Physics or Biology.
That said, the recent computerization of all aspects of business and the creation of virtual economies -like Eve Online, World of Warcraft- and "transparent" markets -like Bitcoin- should provide ample data to develop "real" models of economic behavior against, and I think that many researchers actually already make use of this data.
The theories that will result from this will probably be more like those developed in statistical mechanics though -i.e. making statements about the aggregate behavior of the system- rather than those developed e.g. in electrodynamics, where we usually can predict the behavior of even a single particle. Would love -and be at bit scared- to be proved wrong about this of course :)
> most of the models used in Economics are based on abstract reasoning about "ideal" markets and actors (or more recently on game-theoretic ideas) instead of experimental data.
Essentially it's like pre-Enlightenment Natural Philiosophy, in that it's a discipline that claims to explain the way the world works, but actually explains who has the most sway in getting their ideas accepted.
> ...should provide ample data to develop "real" models of economic behavior against, and I think that many researchers actually already make use of this data.
I suspect reality will get about as much traction against idealogues of e.g. the Chicago School as actual climate science does agains useful idiots like Bjørn Lomborg. It's about providing views that happen to be useful to moneyed interests, not reality.
Great comment. Incidentally, have you come across Deirdre Mccloskey, I've been reading her book 'Bourgeois Dignity: Why Economics Can't Explain the Modern World' and finding it really interesting. Economics is a social science, taking measurements / accumulating data and theorising on that data causes changes in behaviour. Just sitting around talking about economics, or your weekly budget, drives changes in behaviour.
As a layperson with a bit of an interest in economics I don't really see how, as the article says "econ is now a rogue branch of applied math". More like economics uses applied maths, or something like that. It's a 'social science', we can't reveal the laws (let's call them 'habits') of economics like we do The Laws of Nature, because circumstances change -both globally and locally (macro / micro)- and new economic phenomenon emerge.
It'd be interesting to see what future historians have to say about present day economics.
All theories are abstract, that's the whole point of a theory. Without abstraction there's only observation and no reasoning. That's why I'm highly sceptical of the claim that it is possible to understand something simply by "letting the data speak" without "committing to a theory".
The best theories on economics date back much further than the twentieth century. The history of economics is far more interesting than trying to run experiments. A talented economist has to be able to come up with explanations for things they observe, which can then be proven out with further data.
The mistake is in thinking it should or could be a hard science like physics.
Those theoretical models have held up to quite a bit of empirical scrutiny, and almost perfectly so in highly competitive markets like crops, energy (production, not distribution), housing, etc. I would say that the models have remained simplistic because economists have been trained to recognize the conditions when the theoretical models break down.
Competition (or lack of it) is absolutely the number one area where the simplistic models break down, with psychological phenomena being a close second. Even where there is demonstrated and measured irrationality in individual decisions though, in aggregate the distortion effects tend to be minimized.
Outside of these known areas of completixity and modeling deficiency, there have been several major breakthroughs in modeling and understanding other seemingly puzzling factors. Matching problems, search frictions, moral repugnance, game theory, asymmetric information, and discrete choices. There have been huge advances since the 1950's, it just takes some time to catch up with it all.
Experiments are pretty damn crucial to the entire scientific process, right? What if you couldn't do controlled experiments? That's where economics is right now. Until we either get enough data or economists can run actual experiments (ie those that don't just rely on fabricated mathematical models) I don't think we'll have real rigor in economics.
In the meantime, we still have to do our best. People still want ways to predict the future which suffer less from human interference (which is ideally what a mathematical model will let you do). That's still better than just asking people what they think (in some ways).
A lot of simplifying assumptions have to be made to say anything useful about a system which includes billions of intelligent actors trading trillions of dollars. There's obviously room for improvement, and I think that'll happen once people are given the proper tools to make it happen.
I think another reason is that in business you need models you can reason about.
One example is from a bank that advises customers on investment. They have to algorithms doing machine learning on the market, a decision tree and a deep neural net. Over time they can see that the neural net outperforms the decision tree, yet they cannot use the results to advise customers because it is a black box.
Why should you buy this investment? "Because the computer said so" isn`t an answer that makes it sound like you know what you are talking about.
And sometimes that's how economics feels. Absolutely agree with your comment. Coincidentally, I was thinking about this yesterday as I watched one of many economists explain the market on TV. At some level I feel they grab onto one of N plausible ideas and go with it. They have to say something, right? After all, they are economists.
I haven't failed to notice that there are very few (not one?) massively wealthy economists. That has to say something. If you look at the people who have made fortunes they are all practitioners, not theorists. In other words, they "touch and feel" the economy every second of the day for years and understand how it moves, at least at the relevant micro level, to gain advantages.
This one thing has always bothered me about economics. Lots of economists publishing books and claiming all sorts of events and effects and none of them have significant financial success to show for their understanding of the economy. Fake Jedi's who talk about the force but can't use it.
I'd like to see an economist on TV who starts their opinion of the economy in a Trump-esque way with: "I am very rich. I understand the economy and because of that I make $400 million per year".
In terms of using models and simulations, well, we do a lot of FEA, mostly for thermal and fluid dynamics. I had a poster printed and hung in the lab. It reads: "The only person who believes the results of these simulations is the one who wrote the code". I stole that from my friends in aero who always talk about how careful you have to be with aero "codes", particularly at the extremes (low and high RN).
Of course, FEA has gotten better and better over the years, yet it is true that you have to be very careful with how you interpret results. That's why we verify expected results experimentally by building a prototype every N variants of a design based on various criteria. I remember thermal management design to cool over 1,000 Watts of high power LED's concentrated within a small surface area. It took eight months of FEA and dozens of prototypes to get it right. Some of the proto's didn't behave anything like the simulation. Some behaved better. It's an art.
And so my point is that we are likely to have a new crop of economists who will base their opinions on simulations without, perhaps, understanding just how imperfect they can be.
> As a physicist, the models that I encountered in economic theory always left me quite disappointed. The main reason for this was that -like the article says- most of the models used in Economics are based on abstract reasoning about "ideal" markets and actors
I recall my family's PhD. economist relating an anecdote about an colleague explaining that he's making the usual set of assumptions like, say, "people live forever", before doing some math for students. :)
I'm a fan of using physics models for predicting behavior of large groups of people, but economics .... they do occasionally come up with nice statistical tools. Sort of like psychological researchers; those fellows whose papers can only be reproduced around 1/3 of the time.
I would be very surprised if banks and big trading companies aren't doing this already, in order to predict markets. Sharing this information, however, would eliminate their competitive advantage, so I assume they just do it secretly.
There are several problems with social sciences not just economics. Recently, I've read an interview by Yanis Varoufakis[1]. Here's an interesting excerpt:
---
Mariana Mazzucato, University of Sussex – How has the crisis in Greece (its cause and its effects) revealed failings of neoclassical economic theory at both the micro and the macro level?
Varoufakis: The uninitiated may be startled to hear that the macroeconomic models taught at the best universities feature no accumulated debt, no involuntary unemployment and, indeed, no money (with relative prices reflecting a form of barter). Save perhaps for a few random shocks that demand and supply are assumed to quickly iron out, the snazziest models taught to the brightest of students assume that savings automatically turn into productive investment, leaving no room for crises.
It makes it hard when these graduates come face-to-face with reality. They are at a loss, for example, when they see German savings that permanently outweigh German investment while Greek investment outweighs savings during the “good times” (before 2008) but collapses to zero during the crisis.
Moving to the micro level, the observation that, in the case of Greece, real wages fell by 40% but employment dropped precipitously, while exports remained flat, illustrates in Technicolor how useless a microeconomics approach bereft of macro foundations truly is.
----
I really don't know at what level of complexity someone must settle, in order to have a viable mathematical model to predict financial crisis... How many variables do you need? Then again a lot in economics depends on 'perception'. A FinMin will never discuss devaluation of his currency, the moment he does... The currency will drop. How can a mathematical model 'predict' such behaviours?
In my macro economics studies we absolutely included involuntary unemployment in models, this in itself broken into different forms like frictional vs structural unemployment. Similar inclusions for debt and pricing. As for crisis, then what are macro economic stress tests? Economists can be a overly theoretical in places, they have to be in a world of unlimited and often irrational variables, but this is a bit simplistic.
I have quite admired the way Yanis has handled his brief position in Greece. He's clearly an intelligent big picture thinker that was out to serve his peoples interests. This quote seems out of context to my expectations of his usually we'll considered statements. I wonder if there is some loss or context or other soundbite missing here.
Note the contradiction: "no involuntary unemployment and, indeed, no money" followed by "real wages fell by 40% but employment dropped".
The latter statement only contradicts Keynesian economics, which don't have relative prices (Keynesian economics has only real and nominal dollars of GDP) and uses money combined with sticky prices to explain involuntary unemployment.
But although in one sentence he seems to think the Greek experience contradicts Keynesian economics, he turns around and appeals to Keynesian economics a few minutes later: "the 3.5% primary target for 2018 would depress growth today".
Economics has an honesty problem. Financially motivated obfuscation is a problem in other sciences: global warming denialism, sponsored studies claiming that cigarettes aren't carcinogenic, all manner of food health and safety claims, and so on. Generally the evidence is overwhelmingly against them and the battle is mostly over PR and communicating to the public.
Economics is different; arguments that are very convenient to people who are wealthy under the status quo are very difficult to challenge, resulting in failed austerity programmes, "trickle down", and so on.
I partially agree with this view; however, to be fair, this is not a problem with academic economics, but rather with "pop economics".
There is no lack of academic economists who can (and do) prove how austerity programs and trickle-down are wrong; in fact, they're the only ones really leading the charge on this matter in the political world at the moment.
The problem is that policy leaders not trained in advanced economic studies (most of them lawyers by trade, as you would expect in the business of writing laws) are awash in "pop economics", cheap literature peddled by suspect gurus sponsored by interested parties. This creates a hegemonic but distorted view of the field that is then very hard to shake.
Most austerity program fail because there is no true austerity. Outside of Greece which has had to make real cuts simply because it hasn't the money the vast majority of countries which claimed some form of austerity never actually cut spending, some kept increasing it.
In the US it is all a shell game where the deceit of baseline budgeting allows Congress and the President to claim cuts when spending increases. The US could balance its budget in a manner of years if budgeting were honest and they actually reduce spending increases below inflation.
Plenty of economists argue against things like austerity, trickle-down, etc...
The problem is that politicians aren't interested in implementing the most ideal economic policies, they're interested in policies that give the effect they want - which is often at odds with the common good.
To summarize, "I represent truth in the service of mankind. Those with whom I disagree if not stupid, must be acting out of the most base self-interest."
I was just reading the convention speech of the Democratic candidate for president in 1896, William Jennings Bryan. In his speech ( http://historymatters.gmu.edu/d/5354/ ) he says "There are two ideas of government. There are those who believe that if you just legislate to make the well-to-do prosperous, that their prosperity will leak through on those below. The Democratic idea has been that if you legislate to make the masses prosperous their prosperity will find its way up and through every class that rests upon it." So some Democrats had to counter this trickle down nonsense even back then.
As an aside, in the speech he says "we shall fight them to the uttermost, having behind us the producing masses of the nation and the world". Can you imagine a Democratic presidental nominee saying that today, even if by some miracle it was Bernie Sanders? It's impossible - the heirs, and rentiers, and LPs and VCs and "job creators" and other parasites on those of us who work and create wealth have gained too much control of the system. And for the past two weeks signs are around that it could be entering one of its periodic crackups.
I think there's a fundamental misunderstanding of what it is theoretical economists are doing.
A medical scientist runs experiments on rats, to gain insight into what the effects of a certain phenomenon might be on humans. Rats aren't humans, of course, but we think they share important characteristics with humans. Of course, human trials would be better---butbecause there are ethical and practical difficulties in performing human experiments in controlled environments, we are happy to accept the insights that come from studying rats.
Theoretical economics studies the interactions of idealized 'rational' actors. These aren't humans, of course, but like rats, we think they might share important characteristics with humans. Like rational actors, humans have things they want, and do respond to incentives. Economics is largely the study of systems of incentives.
You can gain insight into the systems of rational actors---and insofar as you believe the analogy between rational actors and humans, into human economic systems---by writing equations and proving theorems.
I'm sick of this theme that theoretical economists are these dogmatic lunatics who write equations on the board that have nothing to do with reality. They are in the business of crafting powerful arguments about human nature by analogy, which yes, aren't always accessible to those of us who haven't steeped ourselves in the math, but I think that is no reason to dismiss their insights as nonsense and saying they have a 'math problem'.
As a former cognitive neuroscientist with a master's in psychology, I will just point out that psychology departments look at the massive amount of data demonstrating human irrationality, and conclude that economists are mistaken or wildly naive to extrapolate from "rational actors" human beings.
You don't have a "math problem", you have a "data/science problem". And saly, at least at the micro-level, economists could have disabused themselves of the accuracy of rationality decades ago (e.g., Kahneman and Tversky's work in the 70's).
Much modern economics is like the elaborate math of epicycles, close enough, but still missing key insights. And this won't change until data is valued more.
I entered stats through econometrics. I love econometrics. Econometrics is seriously hampered relative to other applied math fields by the inability to apply large scale casual experiments. For this reason parsimony is soooo important in modelling. Machine learning is the opposite of parsimony. Machine learning is wonderful for many applications, it lets us shrug off a first error of model selection, but it's not a panacea to the math problem and I think it actually might make it much worse.
While pursuing PhD in Economics, I've supported myself working as a data scientist and, like ThePhysicist says, it's fairly disappointing that many are oblivious of datasets, APIs, libraries,..
On the other side, the claim "literary types who lack the talent or training to hack their way through systems of equations" is groundless and mostly fallacious. The "literary types" have grasped many politicized inconsistencies and dedicate their research by deductive reasoning. Most of them are not 9/11 truthers.
My insight in college taking some economics courses side by side higher level physics and engineering courses was that economics has mostly gotten stuck in algebra and has thus far failed to develop its calculus.
The easiest analogies for me to make are to electronics. Talking about cash/debt is like talking about instantaneous voltage. It's sort of interesting, but it isn't doing any actual work. In electronics it is the flow of that voltage, the current, that is more often more important and more interesting and more directly applicable to the "work" of a circuit...
I think you get a very different view of economics if you model it more like electronics circuits than just about any of the other economics models I see discussed. I've often wondered what would happen if someone actually bothered to attempt such a model and apply some deep scientific rigor to it.
comments like this are amazing. you are completely ignorant of the current state of economics yet wonder if economists have bothered to construct dynamic models of the economy. i guess your intermediate macro course didn't get that far...
>That would make the techniques less interesting to many economists, who are usually more concerned about giving policy recommendations than in making forecasts.
Therein lies the real reason for the panoply broken economics models. They are more often used for the purpose of lobbying rather than for impartial prediction.
Much of the profession (particularly the elites) only exists as an intellectual pretext for maintaining and perpetuating existing power structures. It's about as scientific as the departments of Marxist economics were in the former Soviet Union or the Vatican in 15th century Italy.
My beef is when I read economists claims about perfect markets I think, 'Wow! With markets I should be able to solve the traveling salesman problem in polynomial time'
Really, because I always think of dentists as inflating their own professional stature in order to promote their supposedly indispensable remedies, much like economists.
Pure economics should be about applying abstractions to help us understand the world. The world is pretty sophisticated, so I'm fine with mathematically sophisticated abstractions. Even when those abstractions fail intuitively, they can be useful and extended for other subareas of economics or are simply philosophically fascinating. More often than not though, a lot of prominent theory seems impracticable or intractably limited. This is a valid criticism.
Noah Smith is slightly misdirecting his complaints though. If we're looking at applied economics or policy, the amount of math is not intimidating to anyone who has taken a couple of years of non-introductory statistics. The most important econometrics papers of the past few years apply basic regressions with only a few additional valid statistical techniques.
I'd think economics should be about describing systems in terms of cost, and finding how much a certain action will cost or profit the system. There is tons of economy to be found in the natural world, and it is mostly shunned by economists, and left to the naturalists. I don't know of any other science that ignores the natural world like that. And yes I see a problem with that. The probability of being wrong becomes huge. What good is an abstraction if it's wrong?
reminder: noah has never published an academic paper in economics and it looks unlikely that he ever will. he is simply not a competent guide to the field. this entire thread is a perfect example of the blind leading the blind: a journalist (noah) presents an entirely one sided view of the field, and intellectually lazy posters take it as a cue to dump on an entire academic field while freely admitting their ignorance of any of the details.
newsflash: most economists are acutely aware of the imperfections in their models. sure, you can find the blinkered and dogmatic, but that is unsurprising in such a large and varied field. the subject encompasses a serious variety of subjects and methods you (the hn poster) simply know nothing about. for example, machine learning techniques are really nothing new. theorists have been aware of the kahneman/tversky result for decades. please bear this in mind before you lazily declare the intellectual bankruptcy of the entire field.
p.s so-called econophysics was a direct attempt to apply models from hard science to economics and it has been a complete failure, since it lacks an underlying model of human behaviour. turns out this is quite important...
I find it interesting that many economists tend to ignore the limits of their own theories.
For example, the dogma that markets tend toward efficiency.
This is true -- as long as certain axioms are not violated. But they seem to forget about the axioms on which their conclusions are built and march forward as if their conclusions are absolutely true and apply that assumption to problems where they simply do not hold because the axioms on which they are built are violated.
To continue with the 'markets tend toward efficiency' conclusion: it does not hold when there are negative expectations for a party for not making a transaction. And they invented a fudge factor to get around this a bit (that still doesn't work for all cases) which is to make sure the equations when taken in aggregate do not generally lead to negative expectations for not making a transaction. This holds in many more cases but still not all.
Where does it fail and why does it matter? Well, the axioms underlying the 'markets tend toward efficiency' work great in capital markets. They tend to completely fail when pain or death is potentially involved in a transaction (severe negative expectations) but they may look like they should work and you can find limited cases that do work. So, basically war or crime or relationships or corruption or or poverty or healthcare or any other of those human interactions pain or death tend to come into play. A huge swath of sectors where politicians and special interests and the economists that inform them try to craft policy to fit a misapplied theory. Yet, economists march on pretending the theory holds from the axioms to the conclusions and on to the further conclusions built on those hold because 'mathematics' -- who can argue with mathematics?
> Their overview stated that machine learning techniques emphasized causality less than traditional economic statistical techniques, or what's usually known as econometrics. In other words, machine learning is more about forecasting than about understanding the effects of policy.
And it's true. Economists care more about forecasting than 'understanding the effects of policy' because the money is to be made forecasting while working for a bank or a large corporation.
Economics can't be codified the way physics can because economics models human behaviour (which is constantly changing), not physical laws than can be tested and retested.
I shouldn't even comment on this BS article but here we go, duty calls ( https://xkcd.com/386/ )…
1) First of all, let's make clear that there are no "the economists" and there is no "economics" that could be identified with whatever peeve du jour of "the journalists". Economics as a field, understood as the study of exchange between decision-making agents in more or less large systems[1], is marked by an extreme diversity of approaches and opinions on even basic questions. From my superficial understanding of other fields, this seems to be a big difference between economics and the sciences, and on the other hand rather similar to the humanities. If you're going to criticize something, better make it specific and name names.
2) It is fashionable for "technical" people to scoff at the mathematics used in economic theory (basically, analysis, optimization, linear alg, and measure-theoretic prob), pretending that the problems arise because the tools are too primitive. First, perhaps they should remember that a lot of the foundations of mathematical economics were laid by people way above their paygrade. I'm talking about von Neumann, for example, or Fischer Black. Usually, when smart people do something that does not make sense to you, you take a step back and ask what you might be missing, and usually there is a nontrivial answer to that question. Paul Romer, sorry, is a good economist but he has a BS in math. I would say that's my definition of a "lightweight when it comes to equations". That's not to say he shouldn't criticize, he should! But let's not pretend that there was no reason to "go formal" in economic theorizing or that there were/are easy modeling choices.
3) In my experience, most of the criticism of economic models comes from ignorance of the goal and context of a model and a too literal reading of the math. For example, if you take Markowitz's portfolio optimization (mean vs variance of a linear combination of a multivariate normal), it is correct that individual returns have non-exponentially decaying "heavy" tails, the copula is not (unconditionally) Gaussian, and risk is therefore not captured in variance and correlations (not least because they may in fact be undefined). But that is completely beside the point of the model, which is simply to express the idea that the risk of a portfolio is not necessarily additive and that there is a tradeoff between risk and return. The point being, although it is expressed in mathematical language, it is actually a qualitative model. Once you realize this difference to more descriptive models in the sciences, economic theory starts to make a lot more sense.
4) The real problem of economic theory may be the disconnect between how many people have a stake in it and how many people have the time and leisure and inclination and background to understand what the theorists are actually saying.
5) Side comment and half-reply to ThePhysicist's complaint: The reason to go with the "perfect gas"-type models in economic theory, I think, is that you are dealing with self-interested, utility-maximizing particles or "agents" -> acting particles. The thinking being that if you put in constraints of some form, say a short-sales constraint in a financial market model, you make the model very special. But in the messy real world agents would find a way around this particular constraint eventually. So the unconstrained general equilibrium models are trying to give you a big picture, "this is where the market tends to" type of result. There is so much more to be said on this point but I'm already way above my allocated time for this "duty call"...
6) Last but not least, let me state my opinion that there is nothing inherently noble about science. It is a method to gain knowledge, and it is contingent on the affordances of the field to which it is applied. In economic theory, your basic problem is lack of data. Now that may change in some subfields, and that's great. So the scientific method can be applied in those subfields eventually. But we need answers or opinions today for practical problems. I see economics as akin to philosophy how Russell [2] understood it. Let me quote him:
“Philosophy, as I shall understand the word, is something intermediate between theology and science. Like theology, it consists of speculations on matters as to which definite knowledge has, so far, been unascertainable; but like science, it appeals to human reason rather than to authority, whether that of tradition or that of revelation. All definite knowledge—so I should contend—belongs to science; all dogma as to what surpasses definite knowledge belongs to theology. But between theology and science there is a No Man’s Land, exposed to attack from both sides; this No Man’s Land is philosophy. Almost all the questions of most interest to speculative minds are such as science cannot answer, and the confident answers of theologians no longer seem so convincing as they did in former centuries.”
One should be aware of the limits of this approach but one should also not assume that there was a choice between science and this. If there was, it wouldn't even be a contest! The realm of science may get bigger over time, as more and more data is gathered, but again, we need answers today and in real time, not in 100 years, to decide on regulations, interest rates, portfolios or budgeting decisions. In almost all the big economic questions, your sample size is one and the possibility to experiment is nil. The world is arguably nonstationary on time-scales that matter, economic mechanisms change, and every statistical test and computational experiment is always a joint hypothesis test of your assumptions, about which I said in (1) above that there is and perhaps can be no consensus.
In short, it's not that people don't know there is a problem. It's that there has so far been no better solution.
______________________
[1] I'm just making this definition up on the fly but if someone has a better one, I'm all ears.
[2] B. Russell. A History of Western Philosophy. George Allen & Unwin, 1945.
Economics is like fantasy football league. I predicts the future based on isolated personal preferences and ideologies of the "scientist", optionally sprinkled with small scale observations of data with dubious accuracy.
Considering financial policies are the de-facto social discussion around the Globe, and progress of the human species is being discussed and perceived in financial terms, it is no wonder the role of philosophy has fallen to economists.
One can hope that data-driven economics could improve predictions, but keeping the theory discussion alive is vital, if we are to ever understand our goals as something more than increasing production and consumption (quantitative).
Using the correct set of data, has long been the main issue with economic theory conflicts, and data-driven economics can only make a difference (and a huge one) if that changes.
The maths models used in economics are rarely checked against real-world data, but when they are they often turn out to be wrong.
A major instance of this problem comes from models used for trading, which often assume Gaussian distributions and Brownian-motion behaviors. According to those, extreme moves such as the ones occurring during economic crises such as the one of 2007 are several sigmas away from the mean -- making them supposed to happen perhaps once every 10^10 years. Yet they happen every 20 years.
Taleb's book "Black Swan" is an interesting resource on this topic, for those interested.
gosh, i wonder why those traders use those simple, outdated models to make extraordinarily expensive decisions every singly day when they could just read the "black swan"
I like Mark Blyth's brief summary[1] of this problem. Not only is it important to base your theories on actual data, it is also vitally important to remember that the map is not the territory. All models are an imperfect representation of reality, so they should always be viewed with appropriate scepticism, even when the math seems to work out elegantly.
[+] [-] ThePhysicist|10 years ago|reply
As a physicist, the models that I encountered in economic theory always left me quite disappointed. The main reason for this was that -like the article says- most of the models used in Economics are based on abstract reasoning about "ideal" markets and actors (or more recently on game-theoretic ideas) instead of experimental data. One reason for this is of course that when many of those models were developed (in the 50s or earlier) there simply was no reliable (micro-)data available that one could develop a theory against. Another big problem that kept Economics from taking a more experimental stance towards model generation is of course that until recently it was very hard or outright impossible to conduct large-scale experiments, which are the main instrument to validate (or better, not falsify) a given theory in other disciplines such as Physics or Biology.
That said, the recent computerization of all aspects of business and the creation of virtual economies -like Eve Online, World of Warcraft- and "transparent" markets -like Bitcoin- should provide ample data to develop "real" models of economic behavior against, and I think that many researchers actually already make use of this data.
The theories that will result from this will probably be more like those developed in statistical mechanics though -i.e. making statements about the aggregate behavior of the system- rather than those developed e.g. in electrodynamics, where we usually can predict the behavior of even a single particle. Would love -and be at bit scared- to be proved wrong about this of course :)
[+] [-] rodgerd|10 years ago|reply
Essentially it's like pre-Enlightenment Natural Philiosophy, in that it's a discipline that claims to explain the way the world works, but actually explains who has the most sway in getting their ideas accepted.
> ...should provide ample data to develop "real" models of economic behavior against, and I think that many researchers actually already make use of this data.
I suspect reality will get about as much traction against idealogues of e.g. the Chicago School as actual climate science does agains useful idiots like Bjørn Lomborg. It's about providing views that happen to be useful to moneyed interests, not reality.
[+] [-] TheSpiceIsLife|10 years ago|reply
As a layperson with a bit of an interest in economics I don't really see how, as the article says "econ is now a rogue branch of applied math". More like economics uses applied maths, or something like that. It's a 'social science', we can't reveal the laws (let's call them 'habits') of economics like we do The Laws of Nature, because circumstances change -both globally and locally (macro / micro)- and new economic phenomenon emerge.
It'd be interesting to see what future historians have to say about present day economics.
[+] [-] lottin|10 years ago|reply
[+] [-] brc|10 years ago|reply
The mistake is in thinking it should or could be a hard science like physics.
[+] [-] saosebastiao|10 years ago|reply
Competition (or lack of it) is absolutely the number one area where the simplistic models break down, with psychological phenomena being a close second. Even where there is demonstrated and measured irrationality in individual decisions though, in aggregate the distortion effects tend to be minimized.
Outside of these known areas of completixity and modeling deficiency, there have been several major breakthroughs in modeling and understanding other seemingly puzzling factors. Matching problems, search frictions, moral repugnance, game theory, asymmetric information, and discrete choices. There have been huge advances since the 1950's, it just takes some time to catch up with it all.
[+] [-] pachydermic|10 years ago|reply
In the meantime, we still have to do our best. People still want ways to predict the future which suffer less from human interference (which is ideally what a mathematical model will let you do). That's still better than just asking people what they think (in some ways).
A lot of simplifying assumptions have to be made to say anything useful about a system which includes billions of intelligent actors trading trillions of dollars. There's obviously room for improvement, and I think that'll happen once people are given the proper tools to make it happen.
[+] [-] wodenokoto|10 years ago|reply
I think another reason is that in business you need models you can reason about.
One example is from a bank that advises customers on investment. They have to algorithms doing machine learning on the market, a decision tree and a deep neural net. Over time they can see that the neural net outperforms the decision tree, yet they cannot use the results to advise customers because it is a black box.
Why should you buy this investment? "Because the computer said so" isn`t an answer that makes it sound like you know what you are talking about.
[+] [-] rebootthesystem|10 years ago|reply
And sometimes that's how economics feels. Absolutely agree with your comment. Coincidentally, I was thinking about this yesterday as I watched one of many economists explain the market on TV. At some level I feel they grab onto one of N plausible ideas and go with it. They have to say something, right? After all, they are economists.
I haven't failed to notice that there are very few (not one?) massively wealthy economists. That has to say something. If you look at the people who have made fortunes they are all practitioners, not theorists. In other words, they "touch and feel" the economy every second of the day for years and understand how it moves, at least at the relevant micro level, to gain advantages.
This one thing has always bothered me about economics. Lots of economists publishing books and claiming all sorts of events and effects and none of them have significant financial success to show for their understanding of the economy. Fake Jedi's who talk about the force but can't use it.
I'd like to see an economist on TV who starts their opinion of the economy in a Trump-esque way with: "I am very rich. I understand the economy and because of that I make $400 million per year".
In terms of using models and simulations, well, we do a lot of FEA, mostly for thermal and fluid dynamics. I had a poster printed and hung in the lab. It reads: "The only person who believes the results of these simulations is the one who wrote the code". I stole that from my friends in aero who always talk about how careful you have to be with aero "codes", particularly at the extremes (low and high RN).
Of course, FEA has gotten better and better over the years, yet it is true that you have to be very careful with how you interpret results. That's why we verify expected results experimentally by building a prototype every N variants of a design based on various criteria. I remember thermal management design to cool over 1,000 Watts of high power LED's concentrated within a small surface area. It took eight months of FEA and dozens of prototypes to get it right. Some of the proto's didn't behave anything like the simulation. Some behaved better. It's an art.
And so my point is that we are likely to have a new crop of economists who will base their opinions on simulations without, perhaps, understanding just how imperfect they can be.
[+] [-] fennecfoxen|10 years ago|reply
I recall my family's PhD. economist relating an anecdote about an colleague explaining that he's making the usual set of assumptions like, say, "people live forever", before doing some math for students. :)
Assume a spherical cow: https://en.wikipedia.org/wiki/Spherical_cow
[+] [-] scottlocklin|10 years ago|reply
https://en.wikipedia.org/wiki/Foundations_of_Economic_Analys...
I'm a fan of using physics models for predicting behavior of large groups of people, but economics .... they do occasionally come up with nice statistical tools. Sort of like psychological researchers; those fellows whose papers can only be reproduced around 1/3 of the time.
[+] [-] donkeyd|10 years ago|reply
[+] [-] atmosx|10 years ago|reply
---
Mariana Mazzucato, University of Sussex – How has the crisis in Greece (its cause and its effects) revealed failings of neoclassical economic theory at both the micro and the macro level?
Varoufakis: The uninitiated may be startled to hear that the macroeconomic models taught at the best universities feature no accumulated debt, no involuntary unemployment and, indeed, no money (with relative prices reflecting a form of barter). Save perhaps for a few random shocks that demand and supply are assumed to quickly iron out, the snazziest models taught to the brightest of students assume that savings automatically turn into productive investment, leaving no room for crises.
It makes it hard when these graduates come face-to-face with reality. They are at a loss, for example, when they see German savings that permanently outweigh German investment while Greek investment outweighs savings during the “good times” (before 2008) but collapses to zero during the crisis.
Moving to the micro level, the observation that, in the case of Greece, real wages fell by 40% but employment dropped precipitously, while exports remained flat, illustrates in Technicolor how useless a microeconomics approach bereft of macro foundations truly is.
----
I really don't know at what level of complexity someone must settle, in order to have a viable mathematical model to predict financial crisis... How many variables do you need? Then again a lot in economics depends on 'perception'. A FinMin will never discuss devaluation of his currency, the moment he does... The currency will drop. How can a mathematical model 'predict' such behaviours?
[1] https://theconversation.com/varoufakis-in-conversation-with-...
[+] [-] Gustomaximus|10 years ago|reply
I have quite admired the way Yanis has handled his brief position in Greece. He's clearly an intelligent big picture thinker that was out to serve his peoples interests. This quote seems out of context to my expectations of his usually we'll considered statements. I wonder if there is some loss or context or other soundbite missing here.
[+] [-] yummyfajitas|10 years ago|reply
Note the contradiction: "no involuntary unemployment and, indeed, no money" followed by "real wages fell by 40% but employment dropped".
The latter statement only contradicts Keynesian economics, which don't have relative prices (Keynesian economics has only real and nominal dollars of GDP) and uses money combined with sticky prices to explain involuntary unemployment.
But although in one sentence he seems to think the Greek experience contradicts Keynesian economics, he turns around and appeals to Keynesian economics a few minutes later: "the 3.5% primary target for 2018 would depress growth today".
[+] [-] pjc50|10 years ago|reply
Economics is different; arguments that are very convenient to people who are wealthy under the status quo are very difficult to challenge, resulting in failed austerity programmes, "trickle down", and so on.
[+] [-] toyg|10 years ago|reply
There is no lack of academic economists who can (and do) prove how austerity programs and trickle-down are wrong; in fact, they're the only ones really leading the charge on this matter in the political world at the moment.
The problem is that policy leaders not trained in advanced economic studies (most of them lawyers by trade, as you would expect in the business of writing laws) are awash in "pop economics", cheap literature peddled by suspect gurus sponsored by interested parties. This creates a hegemonic but distorted view of the field that is then very hard to shake.
[+] [-] Shivetya|10 years ago|reply
In the US it is all a shell game where the deceit of baseline budgeting allows Congress and the President to claim cuts when spending increases. The US could balance its budget in a manner of years if budgeting were honest and they actually reduce spending increases below inflation.
[+] [-] Mikeb85|10 years ago|reply
The problem is that politicians aren't interested in implementing the most ideal economic policies, they're interested in policies that give the effect they want - which is often at odds with the common good.
[+] [-] JackFr|10 years ago|reply
[+] [-] obrero|10 years ago|reply
I was just reading the convention speech of the Democratic candidate for president in 1896, William Jennings Bryan. In his speech ( http://historymatters.gmu.edu/d/5354/ ) he says "There are two ideas of government. There are those who believe that if you just legislate to make the well-to-do prosperous, that their prosperity will leak through on those below. The Democratic idea has been that if you legislate to make the masses prosperous their prosperity will find its way up and through every class that rests upon it." So some Democrats had to counter this trickle down nonsense even back then.
As an aside, in the speech he says "we shall fight them to the uttermost, having behind us the producing masses of the nation and the world". Can you imagine a Democratic presidental nominee saying that today, even if by some miracle it was Bernie Sanders? It's impossible - the heirs, and rentiers, and LPs and VCs and "job creators" and other parasites on those of us who work and create wealth have gained too much control of the system. And for the past two weeks signs are around that it could be entering one of its periodic crackups.
[+] [-] jevgeni|10 years ago|reply
Unless, of course, you're a politician, in which case it is related to vote count.
[+] [-] gadders|10 years ago|reply
[+] [-] twitchard|10 years ago|reply
A medical scientist runs experiments on rats, to gain insight into what the effects of a certain phenomenon might be on humans. Rats aren't humans, of course, but we think they share important characteristics with humans. Of course, human trials would be better---butbecause there are ethical and practical difficulties in performing human experiments in controlled environments, we are happy to accept the insights that come from studying rats.
Theoretical economics studies the interactions of idealized 'rational' actors. These aren't humans, of course, but like rats, we think they might share important characteristics with humans. Like rational actors, humans have things they want, and do respond to incentives. Economics is largely the study of systems of incentives.
You can gain insight into the systems of rational actors---and insofar as you believe the analogy between rational actors and humans, into human economic systems---by writing equations and proving theorems.
I'm sick of this theme that theoretical economists are these dogmatic lunatics who write equations on the board that have nothing to do with reality. They are in the business of crafting powerful arguments about human nature by analogy, which yes, aren't always accessible to those of us who haven't steeped ourselves in the math, but I think that is no reason to dismiss their insights as nonsense and saying they have a 'math problem'.
[+] [-] KingMob|10 years ago|reply
You don't have a "math problem", you have a "data/science problem". And saly, at least at the micro-level, economists could have disabused themselves of the accuracy of rationality decades ago (e.g., Kahneman and Tversky's work in the 70's).
Much modern economics is like the elaborate math of epicycles, close enough, but still missing key insights. And this won't change until data is valued more.
[+] [-] DanBC|10 years ago|reply
If you use economist math you see a benefit. If you use epidemeologist math you don't.
http://www.cochrane.org/news/educational-benefits-deworming-...
http://www.cochrane.org/CD000371/INFECTN_deworming-school-ch...
http://www.theguardian.com/society/2015/jul/23/research-glob...
http://www.bbc.co.uk/programmes/b0659q1f
[+] [-] RA_Fisher|10 years ago|reply
I entered stats through econometrics. I love econometrics. Econometrics is seriously hampered relative to other applied math fields by the inability to apply large scale casual experiments. For this reason parsimony is soooo important in modelling. Machine learning is the opposite of parsimony. Machine learning is wonderful for many applications, it lets us shrug off a first error of model selection, but it's not a panacea to the math problem and I think it actually might make it much worse.
[+] [-] dovereconomics|10 years ago|reply
A good example of what can be done (http://econprediction.eecs.umich.edu/)
On the other side, the claim "literary types who lack the talent or training to hack their way through systems of equations" is groundless and mostly fallacious. The "literary types" have grasped many politicized inconsistencies and dedicate their research by deductive reasoning. Most of them are not 9/11 truthers.
[+] [-] WorldMaker|10 years ago|reply
The easiest analogies for me to make are to electronics. Talking about cash/debt is like talking about instantaneous voltage. It's sort of interesting, but it isn't doing any actual work. In electronics it is the flow of that voltage, the current, that is more often more important and more interesting and more directly applicable to the "work" of a circuit...
I think you get a very different view of economics if you model it more like electronics circuits than just about any of the other economics models I see discussed. I've often wondered what would happen if someone actually bothered to attempt such a model and apply some deep scientific rigor to it.
[+] [-] nobbis|10 years ago|reply
[+] [-] endzone|10 years ago|reply
[+] [-] crdoconnor|10 years ago|reply
Therein lies the real reason for the panoply broken economics models. They are more often used for the purpose of lobbying rather than for impartial prediction.
Much of the profession (particularly the elites) only exists as an intellectual pretext for maintaining and perpetuating existing power structures. It's about as scientific as the departments of Marxist economics were in the former Soviet Union or the Vatican in 15th century Italy.
[+] [-] chrismealy|10 years ago|reply
[+] [-] Gibbon1|10 years ago|reply
[+] [-] triangleman|10 years ago|reply
[+] [-] georgeglue1|10 years ago|reply
Noah Smith is slightly misdirecting his complaints though. If we're looking at applied economics or policy, the amount of math is not intimidating to anyone who has taken a couple of years of non-introductory statistics. The most important econometrics papers of the past few years apply basic regressions with only a few additional valid statistical techniques.
[+] [-] runarberg|10 years ago|reply
[+] [-] crdoconnor|10 years ago|reply
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[+] [-] endzone|10 years ago|reply
newsflash: most economists are acutely aware of the imperfections in their models. sure, you can find the blinkered and dogmatic, but that is unsurprising in such a large and varied field. the subject encompasses a serious variety of subjects and methods you (the hn poster) simply know nothing about. for example, machine learning techniques are really nothing new. theorists have been aware of the kahneman/tversky result for decades. please bear this in mind before you lazily declare the intellectual bankruptcy of the entire field.
p.s so-called econophysics was a direct attempt to apply models from hard science to economics and it has been a complete failure, since it lacks an underlying model of human behaviour. turns out this is quite important...
[+] [-] clavalle|10 years ago|reply
For example, the dogma that markets tend toward efficiency.
This is true -- as long as certain axioms are not violated. But they seem to forget about the axioms on which their conclusions are built and march forward as if their conclusions are absolutely true and apply that assumption to problems where they simply do not hold because the axioms on which they are built are violated.
To continue with the 'markets tend toward efficiency' conclusion: it does not hold when there are negative expectations for a party for not making a transaction. And they invented a fudge factor to get around this a bit (that still doesn't work for all cases) which is to make sure the equations when taken in aggregate do not generally lead to negative expectations for not making a transaction. This holds in many more cases but still not all.
Where does it fail and why does it matter? Well, the axioms underlying the 'markets tend toward efficiency' work great in capital markets. They tend to completely fail when pain or death is potentially involved in a transaction (severe negative expectations) but they may look like they should work and you can find limited cases that do work. So, basically war or crime or relationships or corruption or or poverty or healthcare or any other of those human interactions pain or death tend to come into play. A huge swath of sectors where politicians and special interests and the economists that inform them try to craft policy to fit a misapplied theory. Yet, economists march on pretending the theory holds from the axioms to the conclusions and on to the further conclusions built on those hold because 'mathematics' -- who can argue with mathematics?
[+] [-] im3w1l|10 years ago|reply
On the other hand a purely game theoretic model is not satisfactory either because a lot of people wont read about the model, and wont act optimally.
Maybe a two-tiered model, with "fish" and "sharks" could be a solution.
[+] [-] ancap|10 years ago|reply
[+] [-] endzone|10 years ago|reply
[+] [-] Mikeb85|10 years ago|reply
> Their overview stated that machine learning techniques emphasized causality less than traditional economic statistical techniques, or what's usually known as econometrics. In other words, machine learning is more about forecasting than about understanding the effects of policy.
And it's true. Economists care more about forecasting than 'understanding the effects of policy' because the money is to be made forecasting while working for a bank or a large corporation.
Economics can't be codified the way physics can because economics models human behaviour (which is constantly changing), not physical laws than can be tested and retested.
[+] [-] HSO|10 years ago|reply
1) First of all, let's make clear that there are no "the economists" and there is no "economics" that could be identified with whatever peeve du jour of "the journalists". Economics as a field, understood as the study of exchange between decision-making agents in more or less large systems[1], is marked by an extreme diversity of approaches and opinions on even basic questions. From my superficial understanding of other fields, this seems to be a big difference between economics and the sciences, and on the other hand rather similar to the humanities. If you're going to criticize something, better make it specific and name names.
2) It is fashionable for "technical" people to scoff at the mathematics used in economic theory (basically, analysis, optimization, linear alg, and measure-theoretic prob), pretending that the problems arise because the tools are too primitive. First, perhaps they should remember that a lot of the foundations of mathematical economics were laid by people way above their paygrade. I'm talking about von Neumann, for example, or Fischer Black. Usually, when smart people do something that does not make sense to you, you take a step back and ask what you might be missing, and usually there is a nontrivial answer to that question. Paul Romer, sorry, is a good economist but he has a BS in math. I would say that's my definition of a "lightweight when it comes to equations". That's not to say he shouldn't criticize, he should! But let's not pretend that there was no reason to "go formal" in economic theorizing or that there were/are easy modeling choices.
3) In my experience, most of the criticism of economic models comes from ignorance of the goal and context of a model and a too literal reading of the math. For example, if you take Markowitz's portfolio optimization (mean vs variance of a linear combination of a multivariate normal), it is correct that individual returns have non-exponentially decaying "heavy" tails, the copula is not (unconditionally) Gaussian, and risk is therefore not captured in variance and correlations (not least because they may in fact be undefined). But that is completely beside the point of the model, which is simply to express the idea that the risk of a portfolio is not necessarily additive and that there is a tradeoff between risk and return. The point being, although it is expressed in mathematical language, it is actually a qualitative model. Once you realize this difference to more descriptive models in the sciences, economic theory starts to make a lot more sense.
4) The real problem of economic theory may be the disconnect between how many people have a stake in it and how many people have the time and leisure and inclination and background to understand what the theorists are actually saying.
5) Side comment and half-reply to ThePhysicist's complaint: The reason to go with the "perfect gas"-type models in economic theory, I think, is that you are dealing with self-interested, utility-maximizing particles or "agents" -> acting particles. The thinking being that if you put in constraints of some form, say a short-sales constraint in a financial market model, you make the model very special. But in the messy real world agents would find a way around this particular constraint eventually. So the unconstrained general equilibrium models are trying to give you a big picture, "this is where the market tends to" type of result. There is so much more to be said on this point but I'm already way above my allocated time for this "duty call"...
6) Last but not least, let me state my opinion that there is nothing inherently noble about science. It is a method to gain knowledge, and it is contingent on the affordances of the field to which it is applied. In economic theory, your basic problem is lack of data. Now that may change in some subfields, and that's great. So the scientific method can be applied in those subfields eventually. But we need answers or opinions today for practical problems. I see economics as akin to philosophy how Russell [2] understood it. Let me quote him:
“Philosophy, as I shall understand the word, is something intermediate between theology and science. Like theology, it consists of speculations on matters as to which definite knowledge has, so far, been unascertainable; but like science, it appeals to human reason rather than to authority, whether that of tradition or that of revelation. All definite knowledge—so I should contend—belongs to science; all dogma as to what surpasses definite knowledge belongs to theology. But between theology and science there is a No Man’s Land, exposed to attack from both sides; this No Man’s Land is philosophy. Almost all the questions of most interest to speculative minds are such as science cannot answer, and the confident answers of theologians no longer seem so convincing as they did in former centuries.”
One should be aware of the limits of this approach but one should also not assume that there was a choice between science and this. If there was, it wouldn't even be a contest! The realm of science may get bigger over time, as more and more data is gathered, but again, we need answers today and in real time, not in 100 years, to decide on regulations, interest rates, portfolios or budgeting decisions. In almost all the big economic questions, your sample size is one and the possibility to experiment is nil. The world is arguably nonstationary on time-scales that matter, economic mechanisms change, and every statistical test and computational experiment is always a joint hypothesis test of your assumptions, about which I said in (1) above that there is and perhaps can be no consensus.
In short, it's not that people don't know there is a problem. It's that there has so far been no better solution.
______________________
[1] I'm just making this definition up on the fly but if someone has a better one, I'm all ears.
[2] B. Russell. A History of Western Philosophy. George Allen & Unwin, 1945.
[+] [-] _pmf_|10 years ago|reply
[+] [-] Vagelis|10 years ago|reply
One can hope that data-driven economics could improve predictions, but keeping the theory discussion alive is vital, if we are to ever understand our goals as something more than increasing production and consumption (quantitative).
Using the correct set of data, has long been the main issue with economic theory conflicts, and data-driven economics can only make a difference (and a huge one) if that changes.
[+] [-] hrzn|10 years ago|reply
A major instance of this problem comes from models used for trading, which often assume Gaussian distributions and Brownian-motion behaviors. According to those, extreme moves such as the ones occurring during economic crises such as the one of 2007 are several sigmas away from the mean -- making them supposed to happen perhaps once every 10^10 years. Yet they happen every 20 years. Taleb's book "Black Swan" is an interesting resource on this topic, for those interested.
[+] [-] endzone|10 years ago|reply
[+] [-] pdkl95|10 years ago|reply
[1] https://www.youtube.com/watch?v=hmWbkPezgtU